The Definition of Curriculum Areas in Occupational Health Psychology

Across the international educational landscape, numerous higher education institutions (HEIs) offer postgraduate programmes in occupational health psychology (OHP). These seek to empower the next generation of OHP practitioners with the knowledge and skills necessary to advance the understanding and prevention of workplace illness and injury, improve working life and promote healthy work through the application of psychological principles and practices. Among the OHP curricula operated within these programmes there exists considerable variability in the topics addressed. This is due, inter alia, to the youthfulness of the discipline and the fact that the development of educational provision has been managed at the level of the HEI where it has remained undirected by external forces such as the discipline’s representative bodies. Such variability makes it difficult to discern the key characteristics of a curriculum which is important for programme accreditation purposes, the professional development and regulation of practitioners and, ultimately, the long-term sustainability of the discipline. This chapter has as its focus the imperative for and development of consensus surrounding OHP curriculum areas. It begins by examining the factors that are currently driving curriculum developments and explores some of the barriers to such. It then reviews the limited body of previous research that has attempted to discern key OHP curriculum areas. This provides a foundation upon which to describe a study conducted by the current authors that involved the elicitation of subject matter expert opinion from an international sample of academics involved in OHP-related teaching and research on the question of which topic areas might be considered important for inclusion within an OHP curriculum. The chapter closes by drawing conclusions on steps that could be taken by the discipline’s representative bodies towards the consolidation and accreditation of a core curriculum.

Expertise and forms of knowledge in the government of families

This paper examines the relationship between the state and the individual in relation to an aspect of mundane family life – the feeding of babies and young children. The nutritional status of children has long been a matter of national concern and infant feeding is an aspect of family life that has been subjected to substantial state intervention. It exemplifies the imposition upon women the ‘biologico-moral responsibility’ for the welfare of children (Foucault 1991b). The state’s attempts to influence mothers’ feeding practices operate largely through education and persuasion. Through an elaborate state-sponsored apparatus, a strongly medicalised expert discourse is disseminated to mothers. This discourse warns mothers of the risks of certain feeding practices and the benefits of others. It constrains mothers through a series of ‘quiet coercions’ (Foucault 1991c) which seek to render them self-regulating subjects. Using data from a longitudinal interview study, this paper explores how mothers who are made responsible in these medical discourses around child nutrition, engage with, resist and refuse expert advice. It examines, in particular, the rhetorical strategies which mothers use to defend themselves against the charges of maternal irresponsibility that arise when their practices do not conform to expert medical recommendations.

STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.